To obtain the results in tab:maintable, we recruited 147 human raters via Prolific, a crowdsourcing platform for recruiting study participants.
In aggregate, raters evaluated 11,610 pairs of clips randomly sampled from our models, ground truth, baseline models, or a checkpoint from our model training (as seen in par:fidk).
All dances were rendered in the same setting with music sampled uniformly at random.
Raters were asked to select the dance that “looked and felt better overall”.
In addition to computing the raw win rate of our method when evaluated directly against competing methods shown in the table, we also computed each method's Elo rating, which, unlike flat win rate, is able to simultaneously capture the relative quality of more than 2 models [1]}.
